{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "### Interactive Workflow of Principal Component Analysis\n",
    "    \n",
    "#### The University of Texas at Austin, PGE 2020 SURI, Undergraduation Research Internship,\n",
    "    \n",
    " #### Arham Junaid, Undergraduate Student, The University of Texas at Austin,\n",
    " \n",
    " #### [LinkedIn](https://www.linkedin.com/in/arhamjunaid)\n",
    "\n",
    " #### Michael Pyrcz, Associate Professor, University of Texas at Austin,\n",
    "    \n",
    " ##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy),\n",
    "\n",
    "#### Introduction\n",
    "\n",
    "Hello and welcome to my workflow relating to Principal Component Analysis. I built out this well-documented workflow with Prof. Pyrcz as part of a SURI undergraduate research project at The University of Texas at Austin.\n",
    "\n",
    "I am Arham Junaid, currently a student doing his undergraduate studies relating to the Energy industry while exploring various Machine Learning methodologies. Principal Component Analysis is an example of inferential machine learning, essentially a method in which your data can be analyzed for dimensional reduction, relationships between variables and making predictions based on the relationships determined. \n",
    "\n",
    "For more check out Prof. Pyrcz's YouTube channel [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig). For the walkthrough video of this workflow go here [walkthrough](TBD). Here's some basic concepts on Principal Component Analysis.\n",
    "\n",
    "#### Principal Component Analysis\n",
    "\n",
    "Principal Component Analysis one of a variety of methods for dimensional reduction:\n",
    "\n",
    "Dimensional reduction transforms the data to a lower dimension\n",
    "\n",
    "* Given features, $𝑋_1,\\dots,𝑋_𝑚$  we would require ${m \\choose 2}=\\frac{𝑚 \\cdot (𝑚−1)}{2}$ scatter plots to visualize just the two-dimensional scatter plots.\n",
    "\n",
    "* Once we have 4 or more variables understanding our data gets very hard.\n",
    "\n",
    "* Recall the curse of dimensionality, impact inference, modeling and visualization. \n",
    "\n",
    "One solution, is to find a good lower dimensional, $𝑝$,  representation of the original dimensions $𝑚$\n",
    "\n",
    "Benefits of Working in a Reduced Dimensional Representation:\n",
    "\n",
    "1. Data storage / Computational Time\n",
    "\n",
    "2. Easier visualization\n",
    "\n",
    "3. Also takes care of multicollinearity \n",
    "\n",
    "#### Orthogonal Transformation \n",
    "\n",
    "Convert a set of observations into a set of linearly uncorrelated variables known as principal components\n",
    "\n",
    "* The number of principal components ($k$) available are min⁡($𝑛−1,𝑚$) \n",
    "\n",
    "* Limited by the variables/features, $𝑚$, and the number of data\n",
    "\n",
    "Components are ordered\n",
    "\n",
    "* First component describes the larges possible variance / accounts for as much variability as possible\n",
    "* Next component describes the largest possible remaining variance \n",
    "* Up to the maximum number of principal components\n",
    "\n",
    "Eigen Values / Eigen Vectors\n",
    "\n",
    "* The Eigen values are the variance explained for each component. \n",
    "* The Eigen vectors of the data covariance matrix are the principal components and the Eigen  \n",
    "* Out of scope – just making the linkage\n",
    "\n",
    "#### Objective\n",
    "In this workflow, we will explore 4 basic aspects of PCA. These include:\n",
    "\n",
    "  - Orthogonal rotation and the incorporation of eigen math\n",
    "\n",
    "  - Forward and backward transformations\n",
    "\n",
    "  - Variance Explained\n",
    "\n",
    "  - Dimensionality Reduction\n",
    "\n",
    "PCA allows is to find good relationships within lower dimensions to allow for greater representation of a multivariate data set and has benefits including ease of visualization, multicollinearity and a reduction in data storage and computational analysis.\n",
    "\n",
    "It is hoped that this interactive version of PCA will provide greater understanding with regards to the benefit of the method in general application.\n",
    "\n",
    "#### Getting Started\n",
    "Here are the steps to get setup in Python with the GeostatsPy package:\n",
    "1.\tInstall Anaconda 3 on your machine (https://www.anaconda.com/download/).\n",
    "2.\tFrom Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal.\n",
    "3.\tIn the terminal type: pip install geostatspy.\n",
    "4.\tOpen Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality.\n",
    "\n",
    "You will need to copy the data file to your working directory. They are available here:\n",
    "   - Tabular data - unconv_MV_v2.csv at https://git.io/fjmBH.\n",
    "\n",
    "#### Install Packages\n",
    "\n",
    "For this interactive workflow to work, we need to install several packages relating to display features, widgets and data analysis interpretation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os                                                 # to set current working directory \n",
    "from sklearn.decomposition import PCA                     # PCA program from scikit learn (package for machine learning)\n",
    "from sklearn.preprocessing import StandardScaler          # standardize variables to mean of 0.0 and variance of 1.0\n",
    "import pandas as pd                                       # DataFrames and plotting\n",
    "import pandas.plotting as pd_plot                         # matrix scatter plots\n",
    "import numpy as np                                        # arrays and matrix math\n",
    "import matplotlib.pyplot as plt                           # plotting\n",
    "import seaborn as sns\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interactive, interact              # widgets and interactivity\n",
    "from ipywidgets import widgets                            \n",
    "from ipywidgets import Layout\n",
    "from ipywidgets import Label\n",
    "import matplotlib.transforms as transforms\n",
    "import math\n",
    "from ipywidgets import VBox, HBox\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "cmap = plt.cm.inferno"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Data Set\n",
    "Next, let’s go ahead and load the data set we are going to be working with. We’re able to view the variables relating to production.\n",
    "This dataset has variables from 1,000 unconventional wells including:\n",
    " -\twell average porosity\n",
    " - \tlog transform of permeability (to linearize the relationships with other variables)\n",
    " -\taccoustic impedance (kg/m^3 x m/s x 10^6)\n",
    " -\tbrittness ratio (%)\n",
    " -\ttotal organic carbon (%)\n",
    " -\tvitrinite reflectance (%)\n",
    " -\tinitial production 90 day average (MCFPD).\n",
    " -\tscaled production\n",
    " \n",
    "First copy the \"unconv_MV_v2.csv\" comma delimited file from https://github.com/GeostatsGuy/GeoDataSets to your working directory, then run this command to read the file into a DataFrame object (part of Pandas package)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>Por</th>\n",
       "      <th>LogPerm</th>\n",
       "      <th>AI</th>\n",
       "      <th>Brittle</th>\n",
       "      <th>TOC</th>\n",
       "      <th>VR</th>\n",
       "      <th>Production</th>\n",
       "      <th>Prod2Scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15.91</td>\n",
       "      <td>1.67</td>\n",
       "      <td>3.06</td>\n",
       "      <td>14.05</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.85</td>\n",
       "      <td>177.381958</td>\n",
       "      <td>1897.657798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15.34</td>\n",
       "      <td>1.65</td>\n",
       "      <td>2.60</td>\n",
       "      <td>31.88</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1479.767778</td>\n",
       "      <td>2745.732996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>20.45</td>\n",
       "      <td>2.02</td>\n",
       "      <td>3.13</td>\n",
       "      <td>63.67</td>\n",
       "      <td>1.79</td>\n",
       "      <td>2.53</td>\n",
       "      <td>4421.221583</td>\n",
       "      <td>5835.130524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>11.95</td>\n",
       "      <td>1.14</td>\n",
       "      <td>3.90</td>\n",
       "      <td>58.81</td>\n",
       "      <td>0.40</td>\n",
       "      <td>2.03</td>\n",
       "      <td>1488.317629</td>\n",
       "      <td>2132.237219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>19.53</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.57</td>\n",
       "      <td>43.75</td>\n",
       "      <td>1.40</td>\n",
       "      <td>2.11</td>\n",
       "      <td>5261.094919</td>\n",
       "      <td>6282.254735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sample    Por  LogPerm    AI  Brittle   TOC    VR   Production  Prod2Scaled\n",
       "0       1  15.91     1.67  3.06    14.05  1.36  1.85   177.381958  1897.657798\n",
       "1       2  15.34     1.65  2.60    31.88  1.37  1.79  1479.767778  2745.732996\n",
       "2       3  20.45     2.02  3.13    63.67  1.79  2.53  4421.221583  5835.130524\n",
       "3       4  11.95     1.14  3.90    58.81  0.40  2.03  1488.317629  2132.237219\n",
       "4       5  19.53     1.83  2.57    43.75  1.40  2.11  5261.094919  6282.254735"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "#os.chdir(r'C:\\PGE383')\n",
    "#df=pd.read_csv(\"unconv_MV_v2.csv\")\n",
    "df=pd.read_csv(r\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/unconv_MV_v2.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are also able to view some basic summary statistics relating to our variables and transpose the data set to make it easier for us to work with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Por</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>14.950460</td>\n",
       "      <td>3.029634</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>12.857500</td>\n",
       "      <td>14.985000</td>\n",
       "      <td>17.080000</td>\n",
       "      <td>24.65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogPerm</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.398880</td>\n",
       "      <td>0.405966</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>1.390000</td>\n",
       "      <td>1.680000</td>\n",
       "      <td>2.58000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>2.982610</td>\n",
       "      <td>0.577629</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>2.577500</td>\n",
       "      <td>3.010000</td>\n",
       "      <td>3.360000</td>\n",
       "      <td>4.70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brittle</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>49.769980</td>\n",
       "      <td>14.944955</td>\n",
       "      <td>3.030000</td>\n",
       "      <td>39.722500</td>\n",
       "      <td>49.680000</td>\n",
       "      <td>59.170000</td>\n",
       "      <td>93.47000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOC</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.003810</td>\n",
       "      <td>0.504978</td>\n",
       "      <td>-0.260000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>2.71000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VR</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.991170</td>\n",
       "      <td>0.308194</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.810000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.172500</td>\n",
       "      <td>2.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Production</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>2247.295809</td>\n",
       "      <td>1464.256312</td>\n",
       "      <td>2.713535</td>\n",
       "      <td>1191.369560</td>\n",
       "      <td>1976.487820</td>\n",
       "      <td>3023.594214</td>\n",
       "      <td>12568.64413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prod2Scaled</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3237.154325</td>\n",
       "      <td>1507.552730</td>\n",
       "      <td>2.713535</td>\n",
       "      <td>2120.961071</td>\n",
       "      <td>2991.762748</td>\n",
       "      <td>4105.623405</td>\n",
       "      <td>12568.64413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count         mean          std       min          25%  \\\n",
       "Por          1000.0    14.950460     3.029634  5.400000    12.857500   \n",
       "LogPerm      1000.0     1.398880     0.405966  0.120000     1.130000   \n",
       "AI           1000.0     2.982610     0.577629  0.960000     2.577500   \n",
       "Brittle      1000.0    49.769980    14.944955  3.030000    39.722500   \n",
       "TOC          1000.0     1.003810     0.504978 -0.260000     0.640000   \n",
       "VR           1000.0     1.991170     0.308194  0.900000     1.810000   \n",
       "Production   1000.0  2247.295809  1464.256312  2.713535  1191.369560   \n",
       "Prod2Scaled  1000.0  3237.154325  1507.552730  2.713535  2120.961071   \n",
       "\n",
       "                     50%          75%          max  \n",
       "Por            14.985000    17.080000     24.65000  \n",
       "LogPerm         1.390000     1.680000      2.58000  \n",
       "AI              3.010000     3.360000      4.70000  \n",
       "Brittle        49.680000    59.170000     93.47000  \n",
       "TOC             0.995000     1.360000      2.71000  \n",
       "VR              2.000000     2.172500      2.90000  \n",
       "Production   1976.487820  3023.594214  12568.64413  \n",
       "Prod2Scaled  2991.762748  4105.623405  12568.64413  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.iloc[:,1:9]                                  # copy all rows and columns 1 through 9, note 0 column is removed\n",
    "df.describe().transpose()                            # calculate summary statistics for the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can go about ensuring the negative values are removed, namely for TOC and Brittleness, since those are not possible and we can set our data values into a single Data Frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Por</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>14.950460</td>\n",
       "      <td>3.029634</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>12.857500</td>\n",
       "      <td>14.985000</td>\n",
       "      <td>17.080000</td>\n",
       "      <td>24.65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogPerm</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.398880</td>\n",
       "      <td>0.405966</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>1.390000</td>\n",
       "      <td>1.680000</td>\n",
       "      <td>2.58000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>2.982610</td>\n",
       "      <td>0.577629</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>2.577500</td>\n",
       "      <td>3.010000</td>\n",
       "      <td>3.360000</td>\n",
       "      <td>4.70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brittle</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>49.769980</td>\n",
       "      <td>14.944955</td>\n",
       "      <td>3.030000</td>\n",
       "      <td>39.722500</td>\n",
       "      <td>49.680000</td>\n",
       "      <td>59.170000</td>\n",
       "      <td>93.47000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOC</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.006170</td>\n",
       "      <td>0.499838</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>2.71000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VR</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.991170</td>\n",
       "      <td>0.308194</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.810000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.172500</td>\n",
       "      <td>2.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Production</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>2247.295809</td>\n",
       "      <td>1464.256312</td>\n",
       "      <td>2.713535</td>\n",
       "      <td>1191.369560</td>\n",
       "      <td>1976.487820</td>\n",
       "      <td>3023.594214</td>\n",
       "      <td>12568.64413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prod2Scaled</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3237.154325</td>\n",
       "      <td>1507.552730</td>\n",
       "      <td>2.713535</td>\n",
       "      <td>2120.961071</td>\n",
       "      <td>2991.762748</td>\n",
       "      <td>4105.623405</td>\n",
       "      <td>12568.64413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count         mean          std       min          25%  \\\n",
       "Por          1000.0    14.950460     3.029634  5.400000    12.857500   \n",
       "LogPerm      1000.0     1.398880     0.405966  0.120000     1.130000   \n",
       "AI           1000.0     2.982610     0.577629  0.960000     2.577500   \n",
       "Brittle      1000.0    49.769980    14.944955  3.030000    39.722500   \n",
       "TOC          1000.0     1.006170     0.499838  0.000000     0.640000   \n",
       "VR           1000.0     1.991170     0.308194  0.900000     1.810000   \n",
       "Production   1000.0  2247.295809  1464.256312  2.713535  1191.369560   \n",
       "Prod2Scaled  1000.0  3237.154325  1507.552730  2.713535  2120.961071   \n",
       "\n",
       "                     50%          75%          max  \n",
       "Por            14.985000    17.080000     24.65000  \n",
       "LogPerm         1.390000     1.680000      2.58000  \n",
       "AI              3.010000     3.360000      4.70000  \n",
       "Brittle        49.680000    59.170000     93.47000  \n",
       "TOC             0.995000     1.360000      2.71000  \n",
       "VR              2.000000     2.172500      2.90000  \n",
       "Production   1976.487820  3023.594214  12568.64413  \n",
       "Prod2Scaled  2991.762748  4105.623405  12568.64413  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num = df._get_numeric_data()                         # get the numerical values\n",
    "num[num < 0] = 0                                     # truncate negative values to 0.0\n",
    "df.describe().transpose()                            # calculate summary statistics for the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to go about dimensionality reduction, a useful first step is to develop a correlation matrix. The higher the magnitude of correlation between your 2 variables, the more likely it is that your principal component scores will be able to account for a greater variance of your data.\n",
    "\n",
    "* PCA assumes that there is linear correlation between features, this does exist in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=sns.heatmap(df.corr(),annot=True,cmap=cmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the correlation matrix above, can observe a mix of bivariate data, and linear correlation magnitudes and for additional visualization, we can take a look at the matrix scatter plot as we look to narrow down to 2 variables to allow for analysis of our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 64 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd_plot.scatter_matrix(df, alpha = 0.1,              # pandas matrix scatter plot\n",
    "    figsize=(7, 7),color = 'black', hist_kwds={'color':['grey']})\n",
    "plt.suptitle(\"Matrix Scatter Plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interactive Exercise #1: Orthogonal Rotation\n",
    "\n",
    "\n",
    "For our first example, let’s explore the power of orthogonal rotation. \n",
    "* Eigen values and eigen vectors are based on the idea of the rotation of your axis. For our example, 2 variables, Por and TOC, with a relatively high correlation coefficient, were chosen to explore this idea further. \n",
    "* For reduced computational load, only 100 values have been selected but you’re welcome to incorporate more of the data by copying and changing the code. \n",
    "* For principle component scores to be calculated, the data first needs to be standardized. \n",
    "\n",
    "We can have a go at visualizing how the data points alter as the angle of rotation varies and how much of the variance is influenced by rotation coordinate 1 and rotational coordinate 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_data_por_perm = df[[\"Por\",\"TOC\"]]                # extract just por and TOC, 100 samples\n",
    "my_data_por_perm =my_data_por_perm.iloc[0:100]\n",
    "features = ['Por','TOC']\n",
    "x = my_data_por_perm.loc[:,features].values\n",
    "s = StandardScaler()\n",
    "s.fit(x)\n",
    "x = s.transform(x)                                  # standardize the data features to mean = 0, var = 1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our data standardized, we can take a look at a scatter plot of the 2 variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Standardized TOC')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, a = plt.subplots()\n",
    "a.scatter(my_data_por_perm[\"Por\"],my_data_por_perm[\"TOC\"],s=None, c=\"darkorange\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.8, linewidths=1.0, edgecolors=\"black\")    \n",
    "a.set_title('Standardized Porosity vs Standardized TOC'); \n",
    "a.set_xlabel(\"Standardized Porosity\")\n",
    "a.set_ylabel(\"Standardized TOC\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot, there seems to be a seemingly linear trend.\n",
    "\n",
    "We can now visualize how the data is rotated in order to understand the proportion of the variance for each rotated coordinate. The rotational coordinates are determined by simple trigonometric functions, sine and cosine to determine their new location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "global xdata                                        #create global values to allow for orthoganal rotation and visualization\n",
    "global ydata\n",
    "def pc_slider(Angle):\n",
    "    global xdata\n",
    "    global ydata\n",
    "    fig15, ((ax15,ax16)) = plt.subplots(1, 2,figsize=(15,6), constrained_layout=True)\n",
    "    fig15.subplots_adjust(wspace=.5,hspace = .5)\n",
    "    \n",
    "    \n",
    "    base = plt.gca().transData\n",
    "    #print(base)\n",
    "    rot = transforms.Affine2D().rotate_deg(int(Angle))\n",
    "    line=ax16.plot(x[:,0],x[:,1], 'o', transform= rot + base, c = 'black', alpha = 0.3)\n",
    "    \n",
    "    xdata=x[:,0]*math.cos(math.radians(int(Angle)))-x[:,1]*math.sin(math.radians(int(Angle)))\n",
    "    ydata=x[:,1]*math.cos(math.radians(int(Angle)))+x[:,0]*math.sin(math.radians(int(Angle)))\n",
    "    \n",
    "    eigen = np.zeros([2,2])\n",
    "    eigen[0,0] = math.cos(Angle*math.pi/180.0)\n",
    "    eigen[1,0] = math.sin(Angle*math.pi/180.0)\n",
    "    eigen[0,1] = -1*math.sin(Angle*math.pi/180.0)\n",
    "    eigen[1,1] = math.cos(Angle*math.pi/180.0)\n",
    "    \n",
    "    df2 = pd.DataFrame({'x':xdata, 'y':ydata})\n",
    "    data = df2.values\n",
    "    lists=[]\n",
    "    \n",
    "    ydataZeroed = np.zeros(len(ydata))\n",
    "\n",
    "    ax16.plot(xdata, ydataZeroed,\"or\", c = 'red', alpha = 0.3)\n",
    "    ax16.plot(ydataZeroed, ydata,\"or\", c= 'blue', alpha = 0.3)\n",
    "    ax16.set_xlim(left=-3.5, right=3.5)\n",
    "    ax16.set_ylim(bottom=-3.5, top=3.5)\n",
    "    \n",
    "    ax16.set_title(\"Arbitrary Data Projection\");ax16.set_xlabel('Projected Feature 1'); ax16.set_ylabel('Projected Feature 2')\n",
    "    labels = 'Feature 2 Variance', 'Feature 1 Variance'\n",
    "    sizes = []\n",
    "    \n",
    "    print('Your Estimated Principal Component/Eigen Vector #1 = ' + str(eigen[:,0]))\n",
    "    print('Your Estimated Principal Component/Eigen Vector #2 = ' + str(eigen[:,1]))\n",
    "    \n",
    "    sumOfVariance=df2.var()['x']+df2.var()['y']\n",
    "    sizes.append(df2.var()['x']/sumOfVariance)\n",
    "    sizes.append(df2.var()['y']/sumOfVariance)\n",
    "    n = ax15.pie(sizes, autopct='%1.2f%%',colors = ['red','blue'],shadow=True,startangle=90)\n",
    "    n[0][0].set_alpha(0.5); n[0][1].set_alpha(0.5)\n",
    "    ax15.axis('equal')\n",
    "    ax15.legend(sizes, labels=labels,loc='upper left')\n",
    "#    plt.tight_layout()\n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.0, wspace=0.1, hspace=0.1)\n",
    "    plt.show()\n",
    "    \n",
    "x0 = widgets.Text(value='                                   Interactive Feature Projection - Orthogonal Rotation, Arham Junaid and Dr. Michael Pyrcz, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "x1 = widgets.IntSlider(min=0, max = 180, value = 0, description = 'Angle',orientation='horizontal')\n",
    "uik2 = widgets.VBox([x0,x1],)\n",
    "interactive_plot = widgets.interactive_output(pc_slider, {'Angle': x1})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Feature Projection with Arbitrary Rotation\n",
    "\n",
    "#### Arham Junaid, SURI Student, Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "Observed the partitioning of variance over 2 new features through orthogonal projection / rotation.\n",
    "\n",
    "### The Inputs\n",
    "\n",
    "* **Angle**: data rotation angle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "e6ac47b17de14f2b9881ba53a0024e5d",
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      "text/plain": [
       "VBox(children=(Text(value='                                   Interactive Feature Projection - Orthogonal Rota…"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "model_id": "e9b4859ac63f47e8868985ff78e3403e",
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(uik2,interactive_plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now convert our rotated coordinates back in the data frame to do some analysis.\n",
    "\n",
    "Now let's visualize the principal components in a more intuitive manner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "global xdata                                        #create global values to allow for orthoganal rotation and visualization\n",
    "global ydata\n",
    "def pc_slider(Angle):\n",
    "    global xdata\n",
    "    global ydata\n",
    "    fig15, ((ax15,ax16)) = plt.subplots(1, 2,figsize=(15,6), constrained_layout=True)\n",
    "    fig15.subplots_adjust(wspace=.5,hspace = .5)\n",
    "    \n",
    "    base = plt.gca().transData\n",
    "    #print(base)\n",
    "    rot = transforms.Affine2D().rotate_deg(int(Angle))\n",
    "    #line=ax16.plot(x[:,0],x[:,1], 'o', transform= rot + base, c = 'black', alpha = 0.3)\n",
    "    line=ax16.plot(x[:,0],x[:,1], 'o', c = 'black', alpha = 0.3)\n",
    "    \n",
    "    xdata=x[:,0]*math.cos(math.radians(int(Angle)))-x[:,1]*math.sin(math.radians(int(Angle)))\n",
    "    ydata=x[:,1]*math.cos(math.radians(int(Angle)))+x[:,0]*math.sin(math.radians(int(Angle)))\n",
    "    \n",
    "    eigen = np.zeros([2,2])\n",
    "    eigen[0,0] = math.cos(Angle*math.pi/180.0)\n",
    "    eigen[1,0] = math.sin(Angle*math.pi/180.0)\n",
    "    eigen[0,1] = -1*math.sin(Angle*math.pi/180.0)\n",
    "    eigen[1,1] = math.cos(Angle*math.pi/180.0)\n",
    "    \n",
    "    df2 = pd.DataFrame({'x':xdata, 'y':ydata})\n",
    "    data = df2.values\n",
    "    lists=[]\n",
    "    \n",
    "    ydataZeroed = np.zeros(len(ydata))\n",
    "\n",
    "    rotinv = transforms.Affine2D().rotate_deg(int(-Angle)) \n",
    "    ax16.plot(xdata, ydataZeroed,\"or\", c = 'red', alpha = 0.3,transform= rotinv + base)\n",
    "    ax16.plot(ydataZeroed, ydata,\"or\", c= 'blue', alpha = 0.3,transform= rotinv + base)\n",
    "    ax16.set_xlim(left=-3.5, right=3.5)\n",
    "    ax16.set_ylim(bottom=-3.5, top=3.5)\n",
    "    \n",
    "    ax16.set_title(\"Data and Arbitrary Feature Projection Components\");ax16.set_xlabel('Standardized Porosity'); ax16.set_ylabel('Standardized TOC')\n",
    "    labels = 'Feature 2 Variance', 'Feature 1 Variance'\n",
    "    sizes = []\n",
    "    \n",
    "#     print('Your Estimated Principal Component/Eigen Vector #1 = ' + str(eigen[:,0]))\n",
    "#     print('Your Estimated Principal Component/Eigen Vector #2 = ' + str(eigen[:,1]))\n",
    "    \n",
    "    sumOfVariance=df2.var()['x']+df2.var()['y']\n",
    "    sizes.append(df2.var()['x']/sumOfVariance)\n",
    "    sizes.append(df2.var()['y']/sumOfVariance)\n",
    "    n = ax15.pie(sizes, autopct='%1.2f%%',colors = ['red','blue'],shadow=True,startangle=90)\n",
    "    n[0][0].set_alpha(0.5); n[0][1].set_alpha(0.5)\n",
    "    ax15.axis('equal')\n",
    "    ax15.legend(sizes, labels=labels,loc='upper left')\n",
    "    ax15.set_title('Variance for Arbitrary Feature Projection Components')\n",
    "#    plt.tight_layout()\n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.0, wspace=0.1, hspace=0.1)\n",
    "    plt.show()\n",
    "    \n",
    "x0 = widgets.Text(value='                                            Interactive Feature Projection - Orthogonal Rotation, Dr. Michael Pyrcz, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "x1 = widgets.IntSlider(min=0, max = 180, value = 0, description = 'Angle',orientation='horizontal',continuous_update=False)\n",
    "uik2 = widgets.VBox([x0,x1],)\n",
    "interactive_plot = widgets.interactive_output(pc_slider, {'Angle': x1})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Feature Projection with Arbitrary Rotation\n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "Observed the partitioning of variance over 2 new features through orthogonal projection / rotation.\n",
    "\n",
    "### The Inputs\n",
    "\n",
    "* **Angle**: data rotation angle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "4ca3f5ac03b843dea9b3fd4f38e0f40d",
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       "version_minor": 0
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      "text/plain": [
       "VBox(children=(Text(value='                                            Interactive Feature Projection - Orthog…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(uik2,interactive_plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we can see the calculation of the covariance matrix and the proportion of variance explained by each Principal Component Score as determined by PCA. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.707  0.707]\n",
      " [-0.707  0.707]]\n",
      "Variance explained by PC1 and PC2 = [0.8596 0.1404]\n"
     ]
    }
   ],
   "source": [
    "n_components = 2\n",
    "pca = PCA(n_components=n_components)\n",
    "pca.fit(x)\n",
    "print(np.round(pca.components_,3))\n",
    "print('Variance explained by PC1 and PC2 =', np.round(pca.explained_variance_ratio_,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At angle 0, the covariance tells us that both variables have the same amount of influence on the variance. This changes with the angle of rotation. \n",
    "\n",
    "The proportion of variance explained by the first principal component score is 85.96%. \n",
    "\n",
    "**Exercise:** Play around with the rotation above and see what angle provides the variance determined by PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interative Workflow #2: Forward and Backward Transformations\n",
    "\n",
    "Below you can choose an arbitrary value for a new data point, in blue. As you play around with where the new data point is placed, you notice that the data point can only go along the de-standardized line in graph 6. This is a key aspect of PCA relating to how the data is transformed based on the Principal Component scores calculated.\n",
    "\n",
    "Here is a flowchart of the transformations which take place:\n",
    "\n",
    "   ![alt text](flowChartFinal.png \"Title\")\n",
    "   \n",
    "   \n",
    "\n",
    "\n",
    "First, the PCA code is written to calculate the location of data points in each of these state spaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu = np.mean(x, axis=0)\n",
    "sd = np.std(x, axis=0)\n",
    "x_stand = StandardScaler().fit_transform(x)                     # standardize the data features to mean = 0, var = 1.0\n",
    "\n",
    "nComp = 1\n",
    "\n",
    "n_components = 2                                                   # build principal component model with 2 components\n",
    "pca = PCA(n_components=n_components)\n",
    "pca.fit(x)\n",
    "\n",
    "x_trans = pca.transform(x)                                         # calculate principal component scores\n",
    "\n",
    "xtrans2 = x_trans.copy()\n",
    "x_trans[:,1] = 0.0                                                 # zero / remove the 2nd principal component \n",
    "\n",
    "xtrans2[:,0] = 0\n",
    "\n",
    "xhat = pca.inverse_transform(x_trans)                             # reverse the principal component scores to standardized values\n",
    "\n",
    "xhat = np.dot(pca.inverse_transform(xhat)[:,:nComp], pca.components_[:nComp,:])\n",
    "xhat = sd*xhat + mu                                                # remove the standardization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then plot the data points in scatter form in each of the state spaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    " def pc_slider(X, Y):\n",
    "    nComp = 1\n",
    "    f, ((ax201,ax202, ax203), (ax204, ax205, ax206)) = plt.subplots(2, 3,figsize=(13,8))\n",
    "    f.subplots_adjust(wspace=0.5,hspace = 0.3)\n",
    "\n",
    "    ax201.scatter(my_data_por_perm['Por'],my_data_por_perm['TOC'],s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")    \n",
    "    ax201.set_title('1. Porosity vs. TOC'); \n",
    "    ax201.set_xlabel(\"Porosity\"); ax201.set_ylabel(\"TOC\")\n",
    "    ax201.set_xlim([5,25]), ax201.set_ylim([0.0,2.5])\n",
    "    features = ['Por','TOC']\n",
    "    \n",
    "    mu = np.mean(x, axis=0)\n",
    "    sd = np.std(x, axis=0)\n",
    "    stscalar = StandardScaler() \n",
    "    x_stand = stscalar.fit_transform(x)                     # standardize the data features to mean = 0, var = 1.0\n",
    "    temp = np.array([float(X), float(Y)])\n",
    "    temp=temp.reshape(-1, 2)\n",
    "    temp=s.transform(temp)\n",
    "    \n",
    "\n",
    "    ax202.scatter(x_stand[:,0],x_stand[:,1], s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    ax202.set_title('2. Standardized Por vs TOC'); ax202.set_xlabel('Standardized Porosity'); ax202.set_ylabel('Standardized TOC')\n",
    "    ax202.set_xlim([-3,3]), ax202.set_ylim([-3,3])\n",
    "\n",
    "    n_components = 2                                                # build principal component model with 2 components\n",
    "    pca = PCA(n_components=n_components)\n",
    "    pca.fit(x)\n",
    "\n",
    "    x_trans = pca.transform(x)                                   # calculate principal component scores\n",
    "    ax203.scatter(x_trans[:,0],x_trans[:,1],s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    ax203.set_title('3. Principal Component Scores'); ax203.set_xlabel('PC1'); ax203.set_ylabel('PC2')\n",
    "    ax203.set_xlim([-3,3]), ax203.set_ylim([-3,3])\n",
    "    \n",
    "    x_trans[:,1] = 0.0                                              # zero / remove the 2nd principal component \n",
    "\n",
    "    ax204.scatter(x_trans[:,0],x_trans[:,1],s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    ax204.set_title('4. Only 1st Principal Component Scores'); ax204.set_xlabel('PC1'); ax204.set_ylabel('PC2')\n",
    "    ax204.set_xlim([-3,3]), ax204.set_ylim([-3,3])\n",
    "    \n",
    "    xhat = pca.inverse_transform(x_trans)                           # reverse the principal component scores to standardized values\n",
    "    ax205.scatter(xhat[:,0],xhat[:,1],s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    ax205.set_title('5. Reverse PCA'); \n",
    "    ax205.set_xlabel('Standardized POR'); ax205.set_ylabel('Standardized TOC')\n",
    "    ax205.set_xlim([-3,3]), ax205.set_ylim([-3,3])                      \n",
    "\n",
    "    xhat = s.inverse_transform(xhat)\n",
    "    ax206.scatter(xhat[:,0],xhat[:,1],s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    ax206.set_title('6. De-standardized Reverse PCA');\n",
    "    ax206.set_xlabel('Porosity'); ax206.set_ylabel('TOC')\n",
    "    ax206.set_xlim([5,25]), ax206.set_ylim([0.0,2.5])\n",
    "    \n",
    "    ax201.plot(float(X),float(Y),marker = \"+\", c = 'black',markersize = 20,markeredgewidth = 3,alpha = 0.8)\n",
    "    ax202.plot(temp[:,0], temp[:,1],marker = \"+\", c = 'black',markersize = 20,markeredgewidth = 3,alpha = 0.8)\n",
    "    x_trans2= pca.transform(temp)\n",
    "    ax203.plot(x_trans2[:,0], x_trans2[:,1],marker = \"+\", c = 'black',markersize = 20,markeredgewidth = 3,alpha = 0.8)\n",
    "    x_trans2[:,1] = 0.0 \n",
    "    ax204.plot(x_trans2[:,0], x_trans2[:,1],marker = \"+\", c = 'black',markersize = 20,markeredgewidth = 3,alpha = 0.8)\n",
    "    xhat2 = pca.inverse_transform(x_trans2) \n",
    "    ax205.plot(xhat2[:,0],xhat2[:,1],marker = \"+\", c = 'black',markersize = 20,markeredgewidth = 3,alpha = 0.8)\n",
    "    xhat21 = s.inverse_transform(xhat2)\n",
    "    ax206.plot(xhat21[:,0],xhat21[:,1],marker = \"+\", c = 'black',markersize = 20,markeredgewidth = 3,alpha = 0.8)\n",
    "\n",
    "x0 = widgets.Text(value='                          Interactive PCA - Transformation and Reverse Transformation, Arham Junaid and Prof. Michael Pyrcz, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "x1 = widgets.IntSlider(min=5, max = 25, value = 0, description = 'Porosity',orientation='horizontal')\n",
    "x2 = widgets.FloatSlider(min=0, max = 2.5, step=0.1,value = 1, description = 'TOC',orientation='horizontal')\n",
    "x4 = widgets.HBox([x1,x2],)\n",
    "uik3 = widgets.VBox([x0,x4],)\n",
    "interactive_output = widgets.interactive_output(pc_slider, {'X': x1, 'Y': x2})\n",
    "interactive_output.clear_output(wait = True)                       # reduce flickering by delaying plot updating\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Datum Projection with PCA \n",
    "\n",
    "#### Arham Junaid, SURI Student, Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "Move around a datum in original feature space and observe its location over all steps of PCA forward and reverse transformation, including standardization.\n",
    "\n",
    "### The Inputs\n",
    "\n",
    "* **Porosity (%), TOC (total organic carbon %)**: the new datum's values in the original features, prior to standardization/projection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "model_id": "88ca26c3a35e4951be02e26c76b3a734",
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      "text/plain": [
       "VBox(children=(Text(value='                          Interactive PCA - Transformation and Reverse Transformati…"
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     },
     "metadata": {},
     "output_type": "display_data"
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      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(uik3,interactive_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice the transformation, forward and backward, as we notice the  movement of the arbitrary point. In the first graph, the blue dot can move anywhere in the 2D space while in the 6th graph, the movement of the blue point is restricted to a single direction despite returning back to the lower dimensional original space.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#### Interactive Workflow #3: Dimensionality Reduction\n",
    "\n",
    "PCA is all about dimensionality reduction. We can see this from the graphs detailed below and how analysis in reduced dimensions allows for a more compact visualization based on the calculation of the Principal Component scores. \n",
    "\n",
    "This is done by converting our data set into Principal Component scores and retaining *p* Principal Component scores. Here, you may choose your 2 variables to calculate your 2 Principal Component scores.\n",
    "\n",
    "The following can be seen from the output below:\n",
    "1. Visual Distribution of the 2 variables\n",
    "2. Descriptive statistics of the original and transformed data set\n",
    "3. The covariance matrix and the matrix displaying variance explained by each PC score\n",
    "4. Cross plots looking the same after PCA is run indicating the method is working correctly\n",
    "5. The transformation of the data into reduced dimensional space\n",
    "6. The variance explained statistics for each PC score  \n",
    "7. Total variance explained of the 2 selected variables based on the number of PC scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "model_id": "6d3bd9d05e184b6781f8f5bc4fc4b840",
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       "version_minor": 0
      },
      "text/plain": [
       "Text(value='                                     Interactive PCA - Dimensionality Reduction, Arham Junaid and …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var_por = np.var(my_data_por_perm['Por'])\n",
    "var_por_hat = np.var(xhat[:,0])\n",
    "var_logperm = np.var(my_data_por_perm['TOC'])\n",
    "var_logperm_hat = np.var(xhat[:,1])\n",
    "\n",
    "\n",
    "def firstvariable(feature1=\"Por\",\n",
    "                 feature2=\"LogPerm\"):\n",
    "    column1 = feature1; column2 = feature2\n",
    "    my_data_por_perm = df[[column1,column2]]                \n",
    "    my_data_por_perm =my_data_por_perm.iloc[0:100]\n",
    "    \n",
    "    print(\"1: Visual Distribution\")\n",
    "    \n",
    "    f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)\n",
    "    ax1.hist(my_data_por_perm[column1], alpha = 0.2, color = 'red', edgecolor = 'black', bins=20)\n",
    "    ax1.set_title(column1)\n",
    "    ax2.hist(my_data_por_perm[column2], alpha = 0.2, color = 'red', edgecolor = 'black', bins=20)\n",
    "    ax2.set_title(column2)\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "    features = [column1,column2]\n",
    "    x = my_data_por_perm.loc[:,features].values\n",
    "    mu = np.mean(x, axis=0)\n",
    "    sd = np.std(x, axis=0)\n",
    "    x = StandardScaler().fit_transform(x)                     # standardize the data features to mean = 0, var = 1.0\n",
    "    \n",
    "    print(\"2: Original and Transformed Statistics\")\n",
    "    print(\" \")\n",
    "    print(\"Original Mean \"+column1+' = ', np.round(mu[0],2), ', Original Mean '+column2+' = ', np.round(mu[1],2)) \n",
    "    print(\"Original StDev \"+column1+' = ', np.round(sd[0],2), ', Original StDev '+column2+' = ', np.round(sd[1],2)) \n",
    "    print('Mean Transformed '+column1+' = ',np.round(np.mean(x[:,0]),2),', Mean Transformed '+column2+' = ',np.round(np.mean(x[:,1]),2))\n",
    "    print('Variance Transformed '+column1+' = ',np.round(np.var(x[:,0]),2),', Variance Transformed '+column2+' = ',np.var(x[:,1]))\n",
    "    \n",
    "\n",
    "    n_components = 2\n",
    "    pca = PCA(n_components=n_components)\n",
    "    pca.fit(x)\n",
    "    print(\" \")\n",
    "    print(\"3: Covariance and Variance Explained Matrices\")\n",
    "    print(\" \")\n",
    "    print(np.round(pca.components_,3))\n",
    "    print('Variance explained by PC1 and PC2 =', np.round(pca.explained_variance_ratio_,3))\n",
    "    print(\" \")\n",
    "    print(\"4: Original and PCA Cross-plots\")\n",
    "    \n",
    "    \n",
    "    f, (ax101, ax102, ax103) = plt.subplots(1, 3,figsize=(12,2.7))\n",
    "    f.subplots_adjust(wspace=0.7)\n",
    "\n",
    "    ax101.scatter(x[:,0],x[:,1], s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    ax101.set_title('Standardized '+column2+' vs. '+column1); ax101.set_xlabel('Standardized '+column1); ax101.set_ylabel('Standardized '+column2)\n",
    "\n",
    "    x_trans = pca.transform(x)                                # calculate the principal component scores\n",
    "    ax102.scatter(x_trans[:,0],x_trans[:,1],s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    ax102.set_title('Principal Component Scores'); ax102.set_xlabel('PC1'); ax102.set_ylabel('PC2')\n",
    "\n",
    "    x_reverse = pca.inverse_transform(x_trans)                        # reverse the principal component scores to standardized values\n",
    "    ax103.scatter(x_reverse[:,0],x_reverse[:,1],s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    ax103.set_title('Reverse PCA'); ax103.set_xlabel('Standardized '+column1); ax103.set_ylabel('Standardized '+column2)\n",
    "    plt.show()\n",
    "    \n",
    "    nComp = 1\n",
    "    \n",
    "   \n",
    "    print(\"5: PCA State Space Transformation\")\n",
    "\n",
    "    mu = np.mean(x, axis=0)\n",
    "    sd = np.std(x, axis=0)\n",
    "    x_stand = StandardScaler().fit_transform(x)               # standardize the data features to mean = 0, var = 1.0\n",
    "\n",
    "\n",
    "    n_components = 2                                          # build principal component model with 2 components\n",
    "    pca = PCA(n_components=n_components)\n",
    "    pca.fit(x)\n",
    "\n",
    "    x_trans = pca.transform(x)                                # calculate principal component scores\n",
    "\n",
    "    x_trans[:,1] = 0.0                                        # zero / remove the 2nd principal component \n",
    "\n",
    "    xhat = pca.inverse_transform(x_trans)                     # reverse the principal component scores to standardized values\n",
    "\n",
    "    xhat = np.dot(pca.inverse_transform(x)[:,:nComp], pca.components_[:nComp,:])\n",
    "    xhat = sd*xhat + mu                                       # remove the standardization\n",
    "     \n",
    "    f, (ax201, ax206) = plt.subplots(1, 2,figsize=(10,3))\n",
    "    f.subplots_adjust(wspace=0.5,hspace = 0.3)\n",
    "    \n",
    "    ax201.scatter(my_data_por_perm[column1],my_data_por_perm[column2],s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    ax201.set_title(column2+' vs. '+column1); ax201.set_xlabel(column1); ax201.set_ylabel(column2)\n",
    "\n",
    "    ax206.scatter(xhat[:,0],xhat[:,1],s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    ax206.set_title('De-standardized Reverse PCA'); ax206.set_xlabel(column1); ax206.set_ylabel(column2)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "    var_por = np.var(my_data_por_perm[column1]); var_por_hat = np.var(xhat[:,0]);\n",
    "    var_logperm = np.var(my_data_por_perm[column2]); var_logperm_hat = np.var(xhat[:,1]);\n",
    "    #print('Variance '+column1+' = ',np.round(var_por,3),', Variance Reduced Dimensional '+column1+' =',np.round(var_por_hat,3),'Fraction = ',np.round(var_por_hat/var_por,3))\n",
    "    #print('Variance '+column2+' =',np.round(var_por,3),', Variance Reduced Dimensional '+column2+' =',np.round(var_logperm_hat,3),'Fraction = ',np.round(var_logperm_hat/var_logperm,3))\n",
    "\n",
    "    global my_data_f7\n",
    "    my_data_f7=df.copy(deep=True)\n",
    "    \n",
    "    np.set_printoptions(suppress=True)\n",
    "    features = ['Por','LogPerm','AI','Brittle','TOC','VR',\"Production\"]\n",
    "    x_f7 = my_data_f7.loc[:,features].values\n",
    "    mu_f7 = np.mean(x_f7, axis=0)\n",
    "    sd_f7 = np.std(x_f7, axis=0)\n",
    "    x_f7 = StandardScaler().fit_transform(x_f7)\n",
    "\n",
    "    #print(\"Original Means = \", features[:], np.round(mu_f7[:],2)) \n",
    "    #print(\"Original StDevs = \", features[:],np.round(sd_f7[:],2)) \n",
    "    #print('Mean Transformed = ',features[:],np.round(x.mean(axis=0),2))\n",
    "    #print('Variance Transformed = ',features[:],np.round(x.var(axis=0),2))\n",
    "    \n",
    "    print(\"6: Variance Explained by PC Score\")\n",
    "    n_components = 7\n",
    "    pca_f7 = PCA(n_components=n_components)\n",
    "    pca_f7.fit(x_f7)\n",
    "    print('\\n\\nVariance explained by PC1 thru PC7 =', np.round(pca_f7.explained_variance_ratio_,3))\n",
    "    fig5 = plt.figure(figsize=(2,2))\n",
    "    ax = fig5.add_axes([0,0,1,1])\n",
    "    langs = ['PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6', 'PC7']\n",
    "    ax.bar(langs,np.round(pca_f7.explained_variance_ratio_,3))\n",
    "    ax.set_title('Variance Explained by Principal Component'); ax.set_xlabel(\"Principal Component\"); ax.set_ylabel(\"Fraction of Variance\")\n",
    "    \n",
    "    nComp=7\n",
    "    x_hat_dim=[]\n",
    "    while nComp >=1: \n",
    "        temp_xhat_dim = np.dot(pca_f7.transform(x_f7)[:,:nComp], pca_f7.components_[:nComp,:])\n",
    "        temp_xhat_dim= sd_f7*temp_xhat_dim + mu_f7\n",
    "        nComp-=1\n",
    "        x_hat_dim.append(temp_xhat_dim)\n",
    "\n",
    "\n",
    "    f, axes2 = plt.subplots(1, 8, figsize=(20,20))\n",
    "    f.subplots_adjust(wspace=0.7)\n",
    "    columns=['Std. Porosity','Std. Log[Perm.]','Std. Acoustic Imped.','Std. Brittleness','Std. Total Organic C', 'Std. Vit. Reflectance', 'Std. Production']\n",
    "    axes2[0].scatter(my_data_f7[column1],my_data_f7[column2],s=None, c=\"red\",marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    axes2[0].set_title('Original Data'); axes2[0].set_xlabel(column1); axes2[0].set_ylabel(column2)\n",
    "    axes2[0].set_ylim(0.0,3.0); axes2[0].set_xlim(8,22); axes2[0].set_aspect(4.0); \n",
    "    i=1\n",
    "    title=['7 Principal Component','6 Principal Components', '5 Principal Components','4 Principal Components', '3 Principal Components', '2 Principal Components', '1 Principal Components' ]\n",
    "    while i<len(axes2):\n",
    "           axes2[i].scatter(x_hat_dim[i-1][:,0],x_hat_dim[i-1][:,4],s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "           axes2[i].set_title(title[i-1]); axes2[i].set_xlabel(column1); axes2[i].set_ylabel(column2)\n",
    "           axes2[i].set_ylim(0.0,3.0); axes2[i].set_xlim(8,22); axes2[i].set_aspect(4.0)\n",
    "           i+=1\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"7: Total Variance of Chosen Features Explained by PC Score\")\n",
    "    print(\" \")\n",
    "    \n",
    "    i=len(x_hat_dim)-1\n",
    "    while i>=0:\n",
    "        print(title[i]+': Variance '+column1+' = ',np.round(np.var(x_hat_dim[i][:,0])/(sd_f7[0]*sd_f7[0]),2),' Variance '+column2+' = ',np.round(np.var(x_hat_dim[i][:,4])/(sd_f7[4]*sd_f7[4]),2))\n",
    "        i-=1\n",
    "\n",
    "    global df_dim\n",
    "    df_dim=[]\n",
    "    i=len(x_hat_dim)-1\n",
    "    while i>=0:\n",
    "        df_1d = pd.DataFrame(data=x_hat_dim[i],columns=features)  \n",
    "        i-=1\n",
    "        df_dim.append(df_1d)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "x0 = widgets.Text(value='                                     Interactive PCA - Dimensionality Reduction, Arham Junaid and Dr. Michael Pyrcz, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "display(x0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Full PCA Workflow\n",
    "\n",
    "#### Arham Junaid, SURI Student, Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "Select two features from the dataset and perform a complete PCA workflow on these features.\n",
    "\n",
    "### The Inputs\n",
    "\n",
    "* **Feature1, Feature2**: the 2 features to apply to a complete PCA workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df9a7aab89084059bd29c7dca76c422d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='feature1', options=('Por', 'LogPerm', 'AI', 'Brittle', 'TOC', 'VR'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.firstvariable(feature1='Por', feature2='LogPerm')>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(firstvariable,feature1=[\"Por\", \"LogPerm\",\"AI\", \"Brittle\",\"TOC\",\"VR\"],feature2=[\"Por\", \"LogPerm\",\"AI\", \"Brittle\",\"TOC\",\"VR\"], continous_update=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations:\n",
    "* You can see from the bar graph the proportion of the variance of the data which each principal component explains.\n",
    "\n",
    "* Moreover, you can see how the bivariate relationship of the 2 variables improves as more principal components are included in your analysis. \n",
    "\n",
    "* It is interesting to note however that the first principal component alone is able to describe a large percentage of the variance of that variable. For example, the first principal component captures 88% of the Porosity variance and 68% of the Permeability variance of the data set.\n",
    "\n",
    "\n",
    "#### Interactive Workflow #4: Variance Explained\n",
    "As can be seen from the data display above, the more principal components we include, the greater the resemblance of your original data. \n",
    "* That being said, even the first 2 principle components can tell us a significant amount in relation to the 2 variables and their variance. \n",
    "* From the interaction below, the number of principal components can be chosen, and the corresponding overall variance of the data set can be explained. \n",
    "* What isn’t explained is the variance lost, a percentage that is in the minority after selecting a mere 2 principal components alone.\n",
    "\n",
    "Let's start of with a reminder of the original matrix scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 49 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd_plot.scatter_matrix(my_data_f7.drop('Prod2Scaled',axis=1), alpha = 0.1,      # pandas matrix scatter plot\n",
    "figsize=(6, 4),color = 'black', hist_kwds={'color':['grey']})\n",
    "features = ['Por','LogPerm','AI','Brittle','TOC','VR',\"Production\"]\n",
    "x_f7 = my_data_f7.loc[:,features].values\n",
    "plt.suptitle('Original Data')\n",
    "x_f7 = StandardScaler().fit_transform(x_f7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can see how the number of principle component scores affects the matrix scatter plot and the proportion of variance explained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9827b199db894ebabc8306c30e4b4f08",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='                                     Interactive PCA - Variance Explained, Arham Junaid and Dr. Mi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6465e9eaf3b24618864e6bdad494c9fe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=4, description='PCs', max=7, min=1), Output()), _dom_classes=('widget-in…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def pc_slider(PCs):\n",
    "    pd_plot.scatter_matrix(df_dim[PCs-1], alpha = 0.1,                         # pandas matrix scatter plot\n",
    "    figsize=(5, 5),color = 'black', hist_kwds={'color':['grey']})\n",
    "    plt.suptitle(str(PCs)+' Principle Components')\n",
    "    n_components = PCs\n",
    "    pca_f7 = PCA(n_components=n_components)\n",
    "    pca_f7.fit(x_f7)\n",
    "    labels = 'Variance Explained', 'Variance Missing'\n",
    "    sizes = []\n",
    "    sizes.append(sum(np.round(pca_f7.explained_variance_ratio_,3)))\n",
    "    sizes.append(1-sum(np.round(pca_f7.explained_variance_ratio_,3)))\n",
    "    fig1, ax1 = plt.subplots(figsize=(3,3))\n",
    "    \n",
    "    n = ax1.pie(sizes, autopct='%1.2f%%',colors = ['red','blue'],shadow=True,startangle=90)\n",
    "    #ax1.legend(sizes, labels=labels,loc='lower right')\n",
    "    ax1.set_title('Variance for Arbitrary Feature Projection Components')\n",
    "    n[0][0].set_alpha(0.5); n[0][1].set_alpha(0.5)\n",
    "    \n",
    "    ax1.axis('equal')                                                          # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "x0 = widgets.Text(value='                                     Interactive PCA - Variance Explained, Arham Junaid and Dr. Michael Pyrcz, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "display(x0)\n",
    "_=interact(pc_slider,PCs=(1,7), continuous_update=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "This is an interactive workflow displaying the key aspects of Principal Components. Appreciation to **Arham Junaid** for writing this code and providing this documentation as part of the SURI program at The University of Texas at Austin.\n",
    "For more about in-depth workflows on PCA, check out INSERT LINK HERE the YouTube video associated with this interactive workflow.\n",
    "\n",
    "If you want to learn more about data analytics and machine learning in Python, The Texas Center for Geostatistics has many other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations, trend modeling and many other workflows available [here](https://github.com/GeostatsGuy/PythonNumericalDemos), along with a package for geostatistics in Python called [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy). \n",
    "\n",
    "We hope this was helpful.\n",
    "\n",
    "*Arham and Michael*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### More on Arham Junaid:\n",
    "Arham is a world citizen who has resided in 5 countries and has spent time in various industries including Emergency Services, the Automotive industry and the Oil & Gas industry. He is currently pursuing a Mechanical Engineering degree and continues to seek experiences which combine his technical expertise with his leadership and business passion. Check out his LinkedIn below:\n",
    "#### [LinkedIn](https://www.linkedin.com/in/arhamjunaid)\n",
    "\n",
    "#### More on Michael Pyrcz and the Texas Center for Data Analytics and Geostatistics:\n",
    "\n",
    "##### Michael Pyrcz, Associate Professor, University of Texas at Austin\n",
    " \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "    \n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development.\n",
    "    \n",
    "For more about Michael check out these links,\n",
    "    \n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1),\n",
    "    \n",
    "   #### Want to Work Together?\n",
    "   \n",
    "   I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate,\n",
    "    \n",
    "   * Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you!\n",
    "    \n",
    "   * Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "   \n",
    "   * I can be reached at mpyrcz@austin.utexas.edu.\n",
    "   \n",
    "I'm always happy to discuss,\n",
    "   \n",
    "*Michael*\n",
    "  \n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "    \n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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